Multivariate alteration detection using angle thresholds based on a context-sensitive Bayesian network
ZHU Rui1(), LI Yikun1,2,3(), LI Xiaojun1,2,3, YANG Shuwen1,2,3, XIE Jiangling1
1. Faculty of Geomatics，Lanzhou Jiaotong University，Lanzhou 730070，China 2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring，Lanzhou 730070，China 3. Key Laboratory of Science and Technology in Surveying & Mapping，Gansu Province，Lanzhou 730070，China
在遥感图像多变化检测领域中，后验概率空间变化向量分析（change vector analysis in posterior probability space，CVAPS）是一种得到广泛使用的变化检测方法。然而，CVAPS利用支持向量机来估计遥感图像像素的后验概率向量，易受到遥感图像中同物异谱、异物同谱、混合像元等因素的影响，从而难以准确估计复杂像元的后验概率向量的强度和方向，并影响了其后多元变化检测的精度。因此，文章在CVAPS的框架下，提出了一种采用模糊C均值聚类分解混合像元，并耦合上下文敏感的贝叶斯网络，使用角度阈值进行多变化类型检测的方法。当夹角小于一定阈值时，则判定该像素为该标准变化向量所代表的变化类型。实验结果证明该算法具有较高变化检测性能，取得了高于对比算法的精度。
In the field of multivariate alteration detection （MAD） of remote sensing images，change vector analysis in posterior probability space （CVAPS） is a widely used method. However，the CVAPS，which employs support vector machines to estimate the posterior probability vectors of remote sensing image pixels，is susceptible to various factors such as different objects with the same spectrum，the same object with different spectra，and mixed pixels in remote sensing images. These factors make it difficult to accurately estimate the magnitude and direction of the posterior probability vectors of complex pixels，consequently affecting the accuracy of multivariate alteration detection. Therefore，under the framework of CVAPS，this paper proposed a MAD method using angle thresholds，which employed the fuzzy C-means clustering to decompose mixed pixels and coupled a context-sensitive Bayesian network. When the angle is less than a certain threshold，the pixel is identified as the change type represented by the standard change vector. Experimental results show that the proposed algorithm exhibited superior alteration detection performance，achieving higher change detection accuracy than other algorithms.
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ZHU Rui, LI Yikun, LI Xiaojun, YANG Shuwen, XIE Jiangling. Multivariate alteration detection using angle thresholds based on a context-sensitive Bayesian network. Remote Sensing for Natural Resources, 2025, 37(5): 131-140.
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2025, Vol. 37 
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